Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling.

We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients' caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed.

rsHRF: A toolbox for resting-state HRF estimation and deconvolution.

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.